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                        数据分析
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                            <b>1.</b>
                        
                        Python数据分析内容
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                            <b>2.</b>
                        
                        python数据分析环境和工具
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                            <b>2.1.</b>
                        
                        Python数据课程 软件和环境安装
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                            <b>2.2.</b>
                        
                        python发行版
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                            <b>2.3.</b>
                        
                        交互式编辑器-JupyterNotebook
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                            <b>2.3.1.</b>
                        
                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                            <b>2.4.1.</b>
                        
                        pip和Virtualenv
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                            <b>2.5.</b>
                        
                        标记语言：Markdown
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                            <b>2.5.1.</b>
                        
                        Markdown语法
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                            <b>2.5.2.</b>
                        
                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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            <span><b>4.</b> 数据分析库的操作</span>
            
            
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                            <b>4.1.</b>
                        
                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                            <b>4.3.</b>
                        
                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
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        <li class="chapter " data-level="4.4.4" data-path="数据分析库的操作/8Pandas分组聚合1.html">
            
                
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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        <li class="chapter " data-level="4.4.6" data-path="数据分析库的操作/10Pandas数据规整-清理.html">
            
                
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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        <li class="chapter " data-level="4.4.7" data-path="数据分析库的操作/11Pandas数据规整-转换.html">
            
                
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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        <li class="chapter " data-level="4.4.8" data-path="数据分析库的操作/16Pandas数据规整-合并.html">
            
                
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
                    </a>
            
            
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            </ul>
            
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        <li class="chapter " data-level="4.5" data-path="数据分析库的操作/13Pandas数据规整-重塑和轴向旋转.html">
            
                
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
                    </a>
            
            
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        <li class="chapter " data-level="4.5.1" data-path="数据分析库的操作/13.1透视表和交叉表.html">
            
                
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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        <li class="chapter " data-level="5" data-path="Python可视化/绘图库-Matplotlib.html">
            
                
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                            <b>5.</b>
                        
                        Python可视化
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        <li class="chapter " data-level="5.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见图表.html">
            
                
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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        <li class="chapter " data-level="5.1.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见设置和操作.html">
            
                
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
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        <li class="chapter " data-level="5.3" data-path="Python可视化/绘图库-Matplotlib/2Matplotlib-图像组件.html">
            
                
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                            <b>5.3.</b>
                        
                        提升：绘图组件
                    </a>
            
            
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        <li class="chapter " data-level="5.4" data-path="Python可视化/绘图库-Matplotlib/3Matplotlib-高级绘图.html">
            
                
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
                    </a>
            
            
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        <li class="chapter " data-level="5.5" data-path="Python可视化/绘图库-Matplotlib/4数学计算展示图像.html">
            
                
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
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        <li class="chapter " data-level="5.6" data-path="Python可视化/绘图库-Matplotlib/5注意事项.html">
            
                
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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        <li class="chapter " data-level="5.7" data-path="Python可视化/绘图库-Matplotlib/6pylab.html">
            
                
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                            <b>5.7.</b>
                        
                        拓展：pylab
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        <li class="chapter " data-level="6" data-path="数据分析必备知识点/数据分析必备知识点汇集.html">
            
                
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                            <b>6.</b>
                        
                        数据分析必备知识点
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        <li class="chapter " data-level="7" data-path="数据分析必备知识点/数据分析流程.html">
            
                
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                            <b>7.</b>
                        
                        数据分析流程
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<li>Pandas&#xFF1A;&#x6570;&#x636E;&#x5206;&#x6790;&#x5E93;</li>
<li>Numpy&#xFF1A;&#x6570;&#x503C;&#x8BA1;&#x7B97;&#x5E93;</li>
<li>Matplotlib&#xFF1A;&#x7ED8;&#x56FE;&#x5E93;&#xFF0C;&#x6570;&#x636E;&#x53EF;&#x89C6;&#x5316;</li>
<li>scikit-learn&#xFF1A;&#x673A;&#x5668;&#x5B66;&#x4E60;&#x5E93;</li>
</ul>
<hr>
<h2 id="&#x5B66;&#x4E60;python&#x6570;&#x636E;&#x8BFE;&#x7A0B;&#x7684;&#x57FA;&#x7840;">&#x5B66;&#x4E60;Python&#x6570;&#x636E;&#x8BFE;&#x7A0B;&#x7684;&#x57FA;&#x7840;</h2>
<p>Python&#x57FA;&#x672C;&#x8BED;&#x6CD5;&#x5373;&#x53EF;</p>
<hr>
<h2 id="&#x5982;&#x4F55;&#x5B66;&#x4E60;">&#x5982;&#x4F55;&#x5B66;&#x4E60;</h2>
<ul>
<li>&#x6DF1;&#x523B;&#x8BA4;&#x8BC6;Python&#x6570;&#x636E;&#x5B66;&#x79D1;&#xFF0C;&#x57F9;&#x517B;&#x5BF9;&#x6570;&#x636E;&#x548C;&#x4FE1;&#x606F;&#x7684;&#x5174;&#x8DA3;</li>
<li>&#x6293;&#x91CD;&#x70B9;&#xFF0C;&#x957F;&#x5C3E;&#x5B9A;&#x5F8B;&#xFF0C;&#x6293;&#x5927;&#x653E;&#x5C0F;</li>
<li>&#x7EF4;&#x62A4;&#x81EA;&#x5DF1;&#x7684;&#x6280;&#x672F;&#x6587;&#x6863;&#xFF0C;&#x4F5C;&#x7535;&#x5B50;&#x7B14;&#x8BB0;<ul>
<li>Markdown</li>
<li>Gitbook</li>
</ul>
</li>
<li>&#x6848;&#x4F8B;&#xFF0C;&#x4F5C;&#x4E3A;&#x5DE5;&#x4F5C;&#x7ECF;&#x9A8C;</li>
<li>&#x7EC3;&#x4E60;&#xFF0C;&#x518D;&#x7EC3;&#x4E60;</li>
</ul>
<hr>
<h2 id="&#x4E3A;&#x4EC0;&#x4E48;&#x5B66;python&#x6570;&#x636E;&#x5206;&#x6790;&#xFF1F;">&#x4E3A;&#x4EC0;&#x4E48;&#x5B66;Python&#x6570;&#x636E;&#x5206;&#x6790;&#xFF1F;</h2>
<h4 id="&#x4E3A;&#x4EC0;&#x4E48;&#x5B66;python">&#x4E3A;&#x4EC0;&#x4E48;&#x5B66;Python?</h4>
<p>Python&#x73B0;&#x5728;&#x662F;&#x6700;&#x6D41;&#x884C;&#x7684;&#x7F16;&#x7A0B;&#x8BED;&#x8A00;</p>
<p>&#x4E3A;&#x4EC0;&#x4E48;&#x6D41;&#x884C;&#xFF1F;&#x7B2C;&#x4E09;&#x6B21;&#x4EBA;&#x5DE5;&#x667A;&#x80FD;&#x6D6A;&#x6F6E;&#x5174;&#x8D77;</p>
<ul>
<li>&#x56FD;&#x5916;&#xFF0C;2012&#x5E74;&#xFF0C;ImageNet&#x5927;&#x89C4;&#x6A21;&#x89C6;&#x89C9;&#x8BC6;&#x522B;&#x6311;&#x6218;&#x8D5B;</li>
<li>&#x56FD;&#x5185;&#xFF0C;2016&#x5E74;3&#x6708;&#xFF0C;AlphaGo&#x4E0E;&#x674E;&#x4E16;&#x77F3;&#x4EBA;&#x673A;&#x5927;&#x6218;&#x83B7;&#x80DC;&#xFF0C;&#x81EA;&#x6B64;&#x4EBA;&#x5DE5;&#x667A;&#x80FD;&#x5728;&#x56FD;&#x5185;&#x5927;&#x706B;</li>
</ul>
<hr>
<h2 id="&#x4E3A;&#x4EC0;&#x4E48;&#x5B66;&#x6570;&#x636E;&#x5206;&#x6790;&#xFF1F;">&#x4E3A;&#x4EC0;&#x4E48;&#x5B66;&#x6570;&#x636E;&#x5206;&#x6790;&#xFF1F;</h2>
<h4 id="&#x4E3A;&#x4E86;&#x5C31;&#x4E1A;&#xFF1A;">&#x4E3A;&#x4E86;&#x5C31;&#x4E1A;&#xFF1A;</h4>
<ul>
<li>&#x8F85;&#x52A9;&#x722C;&#x866B;&#x5C31;&#x4E1A;&#x65B9;&#x5411;</li>
<li>&#x6570;&#x636E;&#x5206;&#x6790;&#x5C31;&#x4E1A;&#x65B9;&#x5411;</li>
</ul>
<p>&#x8F6F;&#x4EF6;&#x5F00;&#x53D1;&#x6570;&#x636E;&#x5206;&#x6790;&#x662F;&#x5B8C;&#x5168;&#x4E0D;&#x540C;&#x7684;&#x4E24;&#x4E2A;&#x884C;&#x4E1A;&#xFF0C;&#x4EE3;&#x8868;&#x4E86;&#x5B8C;&#x5168;&#x4E0D;&#x540C;&#x7684;&#x4E24;&#x79CD;&#x4E16;&#x754C;&#x89C2;</p>
<p>&#x4EBA;&#x7C7B;&#x5B58;&#x5728;&#x7684;&#x76EE;&#x7684;&#xFF1A;</p>
<ul>
<li>&#x8BA4;&#x8BC6;&#x4E16;&#x754C;&#xFF1A;&#x6570;&#x636E;&#x5206;&#x6790;</li>
<li>&#x6539;&#x53D8;&#x4E16;&#x754C;&#xFF1A;&#x8F6F;&#x4EF6;&#x5F00;&#x53D1;</li>
</ul>
<p>&#x4E54;&#x5E03;&#x65AF;&#xFF0C;&#x738B;&#x9633;&#x660E;&#xFF1A;&#x77E5;&#x884C;&#x5408;&#x4E00;</p>
<p>&#x9E21;&#x751F;&#x86CB;&#xFF0C;&#x86CB;&#x751F;&#x9E21;</p>
<ul>
<li>&#x6539;&#x53D8;&#x4E16;&#x754C;&#x6700;&#x91CD;&#x8981;&#xFF1A;<ul>
<li>&#x8F6F;&#x4EF6;&#x5F00;&#x53D1;&#xFF0C;&#x521B;&#x9020;</li>
<li>&#x52AA;&#x529B;&#xFF0C;&#x594B;&#x6597;&#xFF0C;&#x6539;&#x53D8;&#x4E16;&#x754C;&#xFF0C;&#x6539;&#x53D8;&#x81EA;&#x5DF1;</li>
</ul>
</li>
<li>&#x8BA4;&#x8BC6;&#x4E16;&#x754C;&#x6700;&#x91CD;&#x8981;&#xFF1A;<ul>
<li>&#x6570;&#x636E;&#x5206;&#x6790;&#xFF0C;&#x89C2;&#x5BDF;&#xFF0C;&#x7406;&#x89E3;</li>
<li>&#x9759;&#x9759;&#x7684;&#x89C2;&#x5BDF;&#x8FD9;&#x4E2A;&#x4E16;&#x754C;&#xFF0C;&#x7406;&#x89E3;&#x4E16;&#x754C;&#xFF0C;&#x4E86;&#x89E3;&#x81EA;&#x5DF1;</li>
</ul>
</li>
</ul>
<p>&#x4F60;&#x66F4;&#x559C;&#x6B22;&#x54EA;&#x79CD;&#x751F;&#x6D3B;</p>
<hr>
<p>&#x804C;&#x4E1A;&#x7A0B;&#x5E8F;&#x5458;</p>
<ul>
<li>JS</li>
<li>Python</li>
<li>C++</li>
<li>Java</li>
</ul>

                    
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